PERC 2024 Abstract Detail Page
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| Abstract Title: | Large Language Models for Assessment in Physics |
|---|---|
| Abstract Type: | Symposium Talk |
| Abstract: | Recent studies have demonstrated that Large Language Models (LLMs) like GPT-4 can solve approximately 80% of the common homework and exam problems in introductory physics courses. This raises the question of whether LLMs can also assist in evaluating student solutions to these problems. The talk presents initial findings from studies conducted in large-enrollment physics courses at ETH Zurich, a technical university in Europe. These explorations include student evaluations of AI-generated feedback on their handwritten homework-solution derivations and comparisons of grading between teaching assistants and AI for high-stakes handwritten exams in thermodynamics. These findings suggest a scalable approach to providing formative and summative assessments that emphasize reasoning, modeling, and solution strategies, rather than focusing solely on the final result. |
| Parallel Session: | Applications, Opportunities and Challenges of Large Language Models in Physics Education |
Author/Organizer Information | |
| Primary Contact: |
Gerd Kortemeyer ETH Zurich |
